Group Members: Travis, Ira, Micah

Goal: This dashboard presents a multi-regional study of U.S. weather behavior in 2024.
It combines exploratory weather analysis, geographical mapping, and predictive modeling, including a classification machine-learning model.

Exploring wind data using the Meteostat Python API:

Source: Meteostat Python API

Time Period: 2024

Frame: Hourly and Daily

Key Variables
  • wspd: Average wind speed (mph)
  • wdir: Mean wind direction (degrees)
  • temp: Temperature (°F)
  • coco: Condition code
Regional Wind Analysis by Speed and Direction
Hourly Averages 2024 | Data: Meteostat
latitude longitude Wind Statistics
Speed (mph) Direction (°)
Midwest
Cleveland, OH 41.4993 -81.6944 40.1 227.0
Chicago, IL 41.8781 -87.6298 35.4 259.0
Detroit, MI 42.3314 -83.0458 34.8 248.0
Milwaukee, WI 43.0389 -87.9065 33.9 297.0
Minneapolis, MN 44.9778 -93.265 28.2 307.0
Northeast
Buffalo, NY 42.8864 -78.8784 46.0 243.0
Boston, MA 42.3601 -71.0589 38.2 278.0
Philadelphia, PA 39.9526 -75.1652 31.0 297.0
Pittsburgh, PA 40.4406 -79.9959 25.3 299.0
New York, NY 40.7128 -74.006 23.8 300.0
Southeast
Jacksonville, FL 30.3322 -81.6557 32.5 81.0
Miami, FL 25.7617 -80.1918 29.4 81.0
Tampa, FL 27.9506 -82.4572 26.0 49.0
Charlotte, NC 35.2271 -80.8431 22.7 319.0
Atlanta, GA 33.749 -84.388 17.8 357.0
West
Denver, CO 39.7392 -104.9903 30.2 180.0
San Francisco, CA 37.7749 -122.4194 29.9 294.0
Los Angeles, CA 34.0522 -118.2437 24.6 208.0
Portland, OR 45.5152 -122.6784 24.4 333.0
Seattle, WA 47.6062 -122.3321 18.6 191.0
Legend: 🔵North 🔴East 🟡South 🟢West | Darker = Stronger

the Goal of this model is to predict wind vectors at the following International Airports:

  • Pittsburgh International Airport (PIT) – 40.4406, -79.9959
  • Los Angeles International Airport (LAX) – 33.9425, -118.4081
  • Miami International Airport (MIA) – 25.7959, -80.2870
  • Denver International Airport (DEN) – 39.8617, -104.6731
  • Chicago O’Hare International Airport (ORD) – 41.9742, -87.9073
  • Seattle-Tacoma International Airport (SEA) – 47.4502, -122.3088

the features used to predict this are the locations and wind vectors associated with the 5 closest stations

We got the following recorded the following metrics on the test set:

  • R²: 0.7408853538672449
  • RMSE: 5.428108009372506
R²: 0.7408853538672453
RMSE: 5.428108009372502

Model Objective - This model predicts weather condition codes using an extensive batch of features.

Condition Code Groupings
  • Clear: Condition codes 1-2 (Fair weather, clear skies)
  • Cloudy: Condition codes 3-6 (Cloudy, overcast, foggy conditions)
  • Rain: Condition codes 7-13, 17-20 (Various precipitation types including drizzle, rain, thunderstorms)
  • Snow: Condition codes 14-16, 21-22 (Snow, sleet, freezing precipitation)
Note: Storm conditions (codes 23-27) were excluded due to extreme rarity in the dataset Model Construction
  • Algorithm: XGBoost Classifier (selected after comparison with Random Forest and Logistic Regression)
  • City-based splitting 40 training cities tested blindly on 10 test cities
  • Features: 172 engineered features including:
    • Interaction terms: Temperature-pressure ratios, wind-humidity products, etc
    • Lag features: 1, 2, and 3-hour time lag on weather data
    • Differential terms: Pressure changes, humidity changes, wind speed variations
    • Meteorological indices: Heat index, wind chill, vapor pressure deficit
    • Temporal features: Seasonal cycles, solar elevation, daylight indicators
  • Manual class weights applied to address rain/snow underperformance:
    • Clear: 1.5x | Cloudy: 3.0x | Rain: 20.0x | Snow: 325.0x
    • This weighting improved Rain and Snow detection rates but slightly reduced overall accuracy

1. How do weather patterns change by region?

2. What are some case studies of extreme weather?

3. How do geographical features (lakes, oceans, mountains, deserts, plains) impact weather patterns?